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https://github.com/Bancie/TiLearn

Learn to manage and optimize your time.
https://github.com/Bancie/TiLearn

deep-learning machine-learning machine-scheduling operations-research optimization time-management

Last synced: 2 months ago
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Learn to manage and optimize your time.

Host: GitHub
URL: https://github.com/Bancie/TiLearn
Owner: Bancie
License: mit
Created: 2024-06-20T13:52:36.000Z (5 months ago)
Default Branch: main
Last Pushed: 2024-09-06T20:01:46.000Z (2 months ago)
Last Synced: 2024-09-06T20:18:00.964Z (2 months ago)
Topics: deep-learning, machine-learning, machine-scheduling, operations-research, optimization, time-management
Language: Python
Homepage: https://bancie.github.io/TiLearn/
Size: 80.2 MB
Stars: 13
Watchers: 3
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

[![homepage](https://img.shields.io/badge/powered%20by-TiLearn-%237072eb?style=for-the-badge&labelColor=%23555555)](https://bancie.github.io/TiLearn/)
[![PyPI](https://img.shields.io/badge/pypi%20package-0.0.20-%23177bbb?style=for-the-badge&logo=pypi&labelColor=white
)](https://pypi.org/project/TiLearn/)

Welcome to our open-source project focused on applying **Machine Learning (ML)** and **Deep Learning (DL)** techniques to **Machine Scheduling** and **Time Management**, often referred to as **Optimal Processing**. Our goal is to revolutionize the way tasks and processes are managed in various projects by leveraging advanced computational methods to optimize efficiency and productivity.

# Installation

To install TiLearn using PyPI, run the following command:

```
pip install TiLearn
```

Then, in the TiLearn repository that you cloned, simply run:

```
pip install .
```

# Documentation and Usage
For in-depth instructions on installation and building the documentation, see the [TiLearn Documentation Guide and Tutorial](https://bancie.github.io/TiLearn/).

Link: https://bancie.github.io/TiLearn/

# Project Goals

- **Optimized Scheduling**: Develop algorithms that can create optimal schedules for machines, minimizing downtime and maximizing throughput.
- **Predictive Maintenance**: Implement predictive models to foresee and mitigate potential machine failures, ensuring continuous and efficient operations.
- **Time Management**: Utilize deep learning models to enhance time management practices, helping businesses or individuals and team allocate resources more effectively and meet deadlines.
- **Scalability**: Design solutions that are scalable and adaptable to different industrial environments and varying sizes of operations.

# Responsibilities

As part of this open-source project, contributors are encouraged to:

- **Algorithm Development**: Create and refine machine learning and deep learning algorithms tailored to scheduling and time management.
- **Data Collection and Preprocessing**: Gather and preprocess data from various sources to train and validate models.
- **Model Training and Evaluation**: Train models using the collected data and evaluate their performance to ensure accuracy and reliability.
- **Integration and Testing**: Integrate developed models into real-world scheduling systems and conduct extensive testing to validate their effectiveness.
- **Documentation and Support**: Maintain comprehensive documentation of the project, providing clear guidelines for usage and contribution. Assist users and other contributors through forums and issue tracking.

For inquiries, please **contact** me at [[email protected]](https://mail.google.com/mail)